Return predictability and its implications for portfolio selection

This thesis inquires into a range of issues in return predictability and its implications. First, the thesis investigates estimation bias in predictive regressions. This research stresses the importance of accounting for the bias when studying predictability. To tackle the problem of biased estimation, a general and convenient method based on the jackknife technique is proposed. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. Compared with the existing bias-reduction methods in the literature, the proposed method is more stable, robust and flexible. More importantly, it can successfully reduce the estimation bias in long-horizon regressions, whereas the existing bias-reduction methods in the literature cease to work. The effectiveness of the proposed method is demonstrated by simulations and empirical estimates of common predictive models in finance. Empirical results show that the significant predictive variables under ordinary least squares become insignificant after adjusting for the finite-sample bias. These results cast doubt on conclusions drawn in earlier studies on the return predictability by these variables. Next, this thesis examines the predictability of return distributions. It provides detailed insights into predictability of the entire stock and bond return distributions in a quantile regression framework. The difficulty experienced in establishing predictability of the conditional mean through lagged predictor variables does not imply that other parts of the return distribution cannot be predicted. Indeed, many variables are found to have significant but heterogenous effects on the return distributions of stocks and bonds. The thesis establishes a quantile-copula framework for modelling conditional joint return distributions. This framework hinges on quantile regression for marginal return distributions and a copula for the return dependence structure. The framework is shown to be flexible and general enough to model a joint distribution while, at the same time, capturing any non-Gaussian characteristics in both marginal and joint returns. The thesis then explores the implications of return distribution predictability for portfolio selection. A distribution-based framework for portfolio selection is developed which consists of the joint return distribution modelled by the quantile-copula approach and an objective function accommodating higher-order moments. Threshold-accepting optimisation technique is used for obtaining optimal allocation weights. This proposed framework extends traditional moment-based portfolio selection in order to utilise the whole predicted return distribution. The last part of the thesis studies nonlinear dynamics of cross-sectional stock returns using classification and regression trees (CART). The CART models are demonstrated to be a valuable alternative to linear regression analysis in identifying primary drivers of the stock returns. Moreover, a novel hybrid approach combining CART and logistic regression is proposed. This hybrid approach takes advantage of the strengths in both CART and linear parametric models. An empirical application to cross-sectional stock return prediction shows that the hybrid approach captures return dynamics better than either a standalone CART or a logistic model

Essays in asset pricing

My dissertation aims at understanding the impact of uncertainty and disagreement on asset prices. It contains three main chapters. Chapter One gives a general introduction into the topic of partial information and heterogeneous beliefs. Chapter Two explains the link between credit spreads and the heterogeneous formation of expectations in an economy where agents with different perception of economic uncertainty disagree about future cash flows of a defaultable firm. The intertemporal risk-sharing of disagreeing investors gives rise to three testable implications: First, larger belief heterogeneity increases credit spreads and their volatility. Second, it implies a higher frequency of capital structure arbitrage violations. Third, it reduces expected equity returns of low levered firms, but the link can be reversed for high levered firms. We use a data-set of firm-level differences in beliefs, credit spreads, and stock returns to empirically test these predictions. The economic and statistical significance of the intertemporal risk-sharing channel of disagreement is substantial and robust to the inclusion of control variables such as Fama and French, liquidity, and implied volatility factors. Chapter Three studies the link between market-wide uncertainty, difference of opinions and co- movement of stock returns. We show that this link plays an important role in explaining the dynamics of equilibrium volatility and correlation risk premia, the differential cross-sectional pricing of index and individual options, and the risk-return profile of several option trading strategies. We use firm-specific data on analyst forecasts and test the model predictions. We obtain the following novel results: (a) The difference of index and individual volatility risk premia is linked to a counter-cyclical common disagreement component about future earnings; (b) This common component helps to explain the differential pricing of index and individual volatility smiles in the cross-section, as well as the time-series of correlation risk premia extracted from option prices; (c) The time series of returns on straddle and dispersion option portfolios reflects a significant time-varying risk premium, which compensates investors for bearing common disagreement risk; (d) Common disagreement is priced in the cross-section of option strategy returns

Expected returns : An empirical asset pricing study

Doctoral thesis (PhD) - Nord University, 2020publishedVersio

Modeling Asset Prices

As an asset is traded, its varying prices trace out an interesting time series. The price, at least in a general way, reflects some underlying value of the asset. For most basic assets, realistic models of value must involve many variables relating not only to the individual asset, but also to the asset class, the industrial sector(s) of the asset, and both the local economy and the general global economic conditions. Rather than attempting to model the value, we will confine our interest to modeling the price. The underlying assumption is that the price at which an asset trades is a "fair market price" that reflects the actual value of the asset. Our initial interest is in models of the price of a basic asset, that is, not the price of a derivative asset. Usually instead of the price itself, we consider the relative change in price, that is, the rate of return, over some interval of time. The purpose of asset pricing models is not for prediction of future prices; rather the purpose is to provide a description of the stochastic behavior of prices. Models of price changes have a number of uses, including, for investors, optimal construction of portfolios of assets and, for market regulators, maintaining a fair and orderly market. A major motivation for developing models of price changes of given assets is to use those models to develop models of fair value of derivative assets that depend on the given assets.Discrete time series models, continuous time diffusion models, models with jumps, stochastic volatility, GARCH

Alternative portfolio methods

Portfolio optimization in an uncertain environment has great practical value in investment decision process. But this area is highly fragmented due to fast evolution of market structure and changing investor behavior. In this dissertation, four methods are investigated/designed to explore their efficiency under different circumstances. Parametric portfolio decomposes weights by set of factors whose coefficients are uniquely determined via maximizing utility function. A robust bootstrap method is proposed to assist factor selection. If investors exhibit asymmetric aversion of tail risk, pessimistic models on Choquet utility maximization and coherent risk measures acquire superiority. A new hybrid method that inherits advantage of parameterization and tail risk minimization is designed. Mean-variance, which is optimal with elliptical return distribution, should be employed in the case of capital allocation to trading strategies. Nonparametric classifiers may enhance homogeneity of inputs before feeding the optimizer. Traditional factor portfolio can be extended to functional settings by applying FPCA to return curves sorted by factors. Diversification is always achieved by mixing with detected nonlinear components. This research contributes to existing literature on portfolio choice in three-folds: strength and weakness of each method is clarified; new models that outperform traditional approaches are developed; empirical studies are used to facilitate comparison

Every crypto breath in the world : the current global position of the cryptocurrency market and future prediction

This study was motivated by the breakthrough of cryptocurrencies in 2018. The other main reasons behind the motivation are the total market capitalisation of one trillion-dollar diversification possibilities and the lack of preceding scientific research to identify the portfolio diversification possibilities of cryptocurrencies from many angles. Four empirical studies were conducted to provide a holistic view of cryptocurrency as an investment tool. The first study investigated the portfolio diversification possibilities between cryptocurrencies and traditional financial markets. A quantitative method was employed with Cointegration, ARDL bound testing approach, causality, and co-movement testing. Applying Modern portfolio theory to identify the diversification possibilities between the aforementioned markets enabled the study to highlight how investors can reap the benefits of cryptocurrencies. The second study extended the investigation of the portfolio diversification possibilities of cryptocurrency by including precious metals and cryptocurrencies in the same investment basket. Investors switch from traditional investment assets, such as equity and debt market instruments, to precious metal markets to reap benefits. Therefore, this study investigates how cryptocurrency can be an alternative source of investment to include in an investment portfolio. The daily precious metal and cryptocurrency data from 2017 to 2022 was utilised through an ARDL framework to obtain the Cointegration between cryptocurrency, precious metal and across cryptocurrencies. Modern portfolio theory is used to identify the diversification possibilities in this study with different portfolio diversification strategies. The third study clarified the cryptocurrency stakeholders to identify the global perception of cryptocurrency investments. A qualitative method was employed with sentiment analysis, followed by data extractions from the global databases using machine learning algorithms. The study identified the percentage of stakeholder groups' positive, negative, and neutral perceptions of cryptocurrency. The main obstacles hindering cryptocurrency investment growth are the fear of current scams, lack of definitional issues and the absence of a legal framework in some countries. The fourth study included the findings from the first, second and third studies to develop a cryptocurrency predictive model by factoring in macroeconomic variables. Panel data regression with fixed and dynamic effects was employed to analyse the data from 2017 to 2002. The findings suggest the impact of each macroeconomic variable selected in the study for the cryptocurrency price changes while adding more significance to technological variables. The overall findings provide strong support for the portfolio diversification possibilities of cryptocurrencies. Inclusions of the wide range of investment classes, exploring stakeholder perception and highlighting the macroeconomic variables' influence on the cryptocurrency price prediction generate new insights and valuable comparisons about cryptocurrency markets for academia, crypto issuers, investors, government, policymakers, and fund managers to use as an investment and decision-support tools. Keywords: Cryptocurrency, ARDL, Financial Markets, Cointegration, Causality, Portfolio diversification, Precious Metals, Predictive model.Doctor of Philosoph

Complex evolutionary systems in behavioral finance

Traditional finance is built on the rationality paradigm. This chapter discusses simple models from an alternative approach in which financial markets are viewed as complex evolutionary systems. Agents are boundedly rational and base their investment decisions upon market forecasting heuristics. Prices and beliefs about future prices co-evolve over time with mutual feedback. Strategy choice is driven by evolutionary selection, so that agents tend to adopt strategies that were successful in the past. Calibration of "simple complexity models" with heterogeneous expectations to real financial market data and laboratory experiments with human subjects are also discussed.

Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio